Kimi K2.5 Clawdbot 集成：使用 AI 智能体自动化工作流

// Kimi K2.5 API 配置
const KIMI_CONFIG = {
  baseURL: 'https://api.moonshot.ai/v1',
  apiKey: process.env.KIMI_API_KEY,
  model: 'kimi-k2.5', // 以 Moonshot 官方模型列表中的 ID 为准
  maxAgents: 100,  // 启用智能体集群
  contextWindow: 256000
};

// 初始化 Kimi K2.5 客户端
const kimiClient = new KimiClient(KIMI_CONFIG);

{
  "webhook": {
    "name": "Kimi K2.5 智能体处理器",
    "url": "https://your-domain.com/webhooks/kimi-processor",
    "events": ["document.received", "data.batch.ready", "ticket.created"],
    "authentication": {
      "type": "bearer",
      "token": "${KIMI_WEBHOOK_TOKEN}"
    },
    "retryPolicy": {
      "maxAttempts": 3,
      "backoffMultiplier": 2
    }
  }
}

# Kimi K2.5 Clawdbot webhook 处理器
import asyncio
from kimi import KimiSwarm

class KimiClawdbotHandler:
    def __init__(self):
        self.swarm = KimiSwarm(
            max_agents=100,
            coordination_mode="parallel"
        )
    
    async def process_webhook(self, payload):
        """使用智能体集群处理传入的 Clawdbot webhook"""
        
        # 提取任务详情
        task_type = payload['event_type']
        documents = payload['data']['documents']
        
        # 根据任务复杂度部署智能体集群
        if len(documents) > 10:
            return await self._parallel_process(documents)
        else:
            return await self._single_agent_process(documents)
    
    async def _parallel_process(self, documents):
        """使用智能体集群处理大批量"""
        agents = self.swarm.deploy(
            agent_count=min(len(documents), 100),
            task_template="analyze_document",
            coordination_strategy="map_reduce"
        )
        
        results = await agents.process_batch(documents)
        return self._aggregate_results(results)

# Clawdbot 工作流配置
workflow:
  name: "智能文档分析"
  trigger:
    type: webhook
    event: s3.document.uploaded
  steps:
    - name: extract_text
      action: ocr_service
    - name: analyze_with_kimi
      action: http_request
      config:
        url: "${KIMI_API_ENDPOINT}"
        method: POST
        body:
          model: "kimi-k2.5"
          messages:
            - role: system
              content: "分析此文档的关键洞察、风险和行动项。"
            - role: user
              content: "${steps.extract_text.content}"
          swarm_config:
            enabled: true
            agent_count: 10
    - name: route_decision
      action: conditional
      conditions:
        - if: "${analyze_with_kimi.risk_score} > 0.7"
          then: notify_urgent
        - else: archive_standard

// 使用智能体集群的客户支持自动化
async function handleSupportTicket(ticket) {
  const swarm = new KimiSwarm({
    agents: [
      { role: 'intent_classifier', priority: 1 },
      { role: 'solution_researcher', priority: 2 },
      { role: 'response_generator', priority: 3 },
      { role: 'quality_checker', priority: 4 }
    ],
    coordination: 'pipeline'
  });
  
  const result = await swarm.execute({
    ticket_id: ticket.id,
    customer_query: ticket.content,
    history: ticket.thread,
    knowledge_base: await fetchKnowledgeBase()
  });
  
  return {
    response: result.response,
    confidence: result.confidence,
    auto_resolve: result.confidence > 0.9
  };
}

# 使用 Kimi K2.5 的自动代码审查
def review_pull_request(pr_data):
    swarm = KimiSwarm(
        max_agents=50,
        coordination="parallel"
    )
    
    # 将 PR 分成可审查的块
    chunks = split_codebase(pr_data.files)
    
    # 部署审查代理
    reviews = swarm.map_reduce(
        task="code_review",
        items=chunks,
        aggregator="consolidate_reviews"
    )
    
    return {
        "issues": reviews.findings,
        "suggestions": reviews.improvements,
        "security_flags": reviews.security_issues,
        "approval_recommendation": reviews.score > 0.8
    }

# 最大吞吐量配置
parallel_swarm = {
    "mode": "parallel",
    "agent_count": 100,
    "coordination": {
        "type": "master_worker",
        "load_balancing": "adaptive"
    },
    "optimization": {
        "runtime_reduction_target": 0.80,  # 80% 更快
        "max_concurrent_tools": 1500
    }
}

# 带有专业化的顺序处理
pipeline_swarm = {
    "mode": "pipeline",
    "stages": [
        {"name": "ingestion", "agents": 5, "task": "data_validation"},
        {"name": "analysis", "agents": 20, "task": "deep_processing"},
        {"name": "synthesis", "agents": 10, "task": "result_integration"},
        {"name": "validation", "agents": 5, "task": "quality_check"}
    ],
    "handoff_strategy": "streaming"
}

# 生产环境的健壮错误处理
def execute_with_resilience(task, max_retries=3):
    for attempt in range(max_retries):
        try:
            result = kimi_swarm.execute(task)
            
            # 验证结果质量
            if result.confidence < 0.7:
                # 触发回退代理
                result = fallback_agent.reprocess(task)
            
            return result
            
        except KimiAPIError as e:
            if e.code == 429:  # 速率限制
                time.sleep(2 ** attempt)
            elif e.code >= 500:  # 服务器错误
                continue
            else:
                raise
        
        except Exception as e:
            log_error(e, task)
            if attempt == max_retries - 1:
                trigger_manual_review(task)

# 监控仪表板配置
metrics:
  performance:
    - agent_utilization_rate
    - average_task_completion_time
    - swarm_efficiency_ratio
    - context_window_usage
  
  quality:
    - response_accuracy_score
    - human_override_rate
    - error_retry_rate
    - customer_satisfaction
  
  cost:
    - tokens_per_transaction
    - cost_per_automation
    - savings_vs_manual
    - roi_calculation

# 安全数据处理
from cryptography.fernet import Fernet

class SecureKimiHandler:
    def __init__(self):
        self.encryption_key = os.environ['KIMI_DATA_KEY']
    
    def process_sensitive_data(self, encrypted_payload):
        # 解密传入数据
        f = Fernet(self.encryption_key)
        data = f.decrypt(encrypted_payload)
        
        # 使用 Kimi K2.5 处理
        result = kimi_swarm.process(data)
        
        # 存储前重新加密
        return f.encrypt(result.to_json())

# 实施分块处理
swarm_config = {
    "chunk_size": 25,  # 一次处理 25 项
    "chunk_timeout": 240,  # 每块 4 分钟
    "checkpoint_interval": 10  # 每 10 项保存进度
}

# 智能上下文管理
def process_large_document(doc):
    if len(doc.tokens) > 256000:
        # 首先使用摘要代理
        summary = kimi.summarize(doc, target_tokens=200000)
        return kimi.analyze(summary)
    return kimi.analyze(doc)